The present invention relates, in general, to neural networks, and more particularly to adaptation of pretrained neural networks.
A neural network is essentially a pattern recognition apparatus. A common and complex application of such an apparatus is recognition of spoken words or commands. The fundamental challenges inherent in this process are outlined on pages 9 and 10 of "Automatic Speech Recognition", Kai-Fu Lee, published by Kluwer Academic Publishers, Boston, 1989, and which is incorporated herein by reference. The section sets forth four fundamental problems that must be overcome, including lack of learning and adaptation. In this context, lack of learning and adaptation refers to the acoustic properties both of the speaker and of the environment including the sound gathering system.
The prior art speech recognition systems include: hidden Markov models, pure neural network architectures, hybrid systems, and adaptive hybrid systems. Hidden Markov models (HMMs) are incapable of adapting to environmental conditions. Pure neural network architectures do not adequately represent the temporal nature of speech. Hybrid systems use both a neural network and an HMM, but do not use the adaptation properties of neural networks. Previous adaptive hybrid systems require complex training rules. None of these systems provide an easily trained system that can adapt to both the speaker and acoustic environment.
There is a need for a speech recognition system that can adapt to different speakers and changing environments. To be useful, the adaptation process must be both rapid and non-intrusive. The system should readily adapt to a variety of individuals as well as to various speech conditions experienced by those individuals. For example, the system should be able to work even though the speaker has a cold. Ideally the system should also address recognition problems which are analogous to speech recognition such as image recognition.